植物数据联合物种分布建模中几个值得注意的问题

A. Gelfand
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摘要

由于认识到单一物种模型不能考虑物种间预期的依赖/相互作用,联合物种分布模型越来越受到文献的关注。这篇短文提供了讨论,试图阐明在工厂数据的背景下与这种建模相关的五个值得注意的技术问题。在这种情况下,文献中的联合物种分布工作考虑了几种类型的物种数据收集。为了便于讨论,我们将重点放在在场/缺席数据的联合建模上。对于这类数据,主要的建模策略是通过引入潜在的多变量正态随机变量。这些问题涉及以下内容:(i)观测到的存在/缺失数据如何与潜在正态变量联系起来,以及将数据点建模为独立或空间依赖的结果,(ii)在物种分布的空间建模中,点参考和面参考存在/缺失数据的不兼容性,(iii)在评估物种分布方面,独立/边缘建模物种而不是在站点内联合建模物种的影响,(iv)使用潜在多变量正态规范解释物种依赖,以及(v)解释与特定联合物种分布建模规范相关的物种聚类。希望通过尝试澄清这些问题,生态建模者和定量生态学家将能够更好地理解这些不断增长的建模思想集合中隐含的一些微妙之处。在这方面,这篇论文可以作为b[33]最近发表在《生态学与进化方法》(Methods In Ecology and Evolution)上的调查/比较文章的有益补充。
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Some Noteworthy Issues in Joint Species Distribution Modeling for Plant Data
Joint species distribution modeling is attracting increasing attention in the literature these days, recognizing the fact that single species modeling fails to take into account expected dependence/interaction between species. This short paper offers discussion that attempts to illuminate five noteworthy technical issues associated with such modeling in the context of plant data. In this setting, the joint species distribution work in the literature considers several types of species data collection. For convenience of discussion, we focus on joint modeling of presence/absence data. For such data, the primary modeling strategy has been through introduction of latent multivariate normal random variables. These issues address the following: (i) how the observed presence/absence data is linked to the latent normal variables as well as the resulting implications with regard to modeling the data sites as independent or spatially dependent, (ii) the incompatibility of point referenced and areal referenced presence/absence data in spatial modeling of species distribution, (iii) the effect of modeling species independently/marginally rather than jointly within site, with regard to assessing species distribution, (iv) the interpretation of species dependence under the use of latent multivariate normal specification, and (v) the interpretation of clustering of species associated with specific joint species distribution modeling specifications. It is hoped that, by attempting to clarify these issues, ecological modelers and quantitative ecologists will be able to better appreciate some subtleties that are implicit in this growing collection of modeling ideas. In this regard, this paper can serve as a useful companion piece to the recent survey/comparison article by [33] in Methods in Ecology and Evolution.
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